从双手人类演示中学习符号与亚符号时序任务约束
Learning Symbolic and Subsymbolic Temporal Task Constraints from Bimanual Human Demonstrations
AI总结：
本文提出模型驱动方法，从多组双手人类演示中联合学习基于动作关键点时间差分布的亚符号时序约束，并用模糊逻辑导出符号约束，实现优于纯符号方法的双手同步执行。
摘要：
从人类演示中学习双手操作的任务模型并在机器人上执行，应当考虑动作之间的时序约束。这包括：（i）符号层面的约束，例如先后关系或执行过程中的时间重叠；以及（ii）亚符号层面的约束，例如不同动作的持续时间，或其在时间上的起点和终点。这类时序约束对于时序规划、推理，以及在双手机器人上精确把握双手动作的执行时机至关重要。在我们此前的工作中，我们研究了符号层面时序任务约束的学习，并展示了机器人如何利用这一知识在执行过程中应对故障。在本工作中，我们提出一种新颖的模型驱动方法，用于从多个双手人类演示中联合学习符号与亚符号时序任务约束。我们的主要贡献包括：一个时序任务模型的亚符号基础，该模型基于语义动作关键点之间时间差的分布来描述任务中动作的时序联系；以及一种基于模糊逻辑的方法，用于从该表示中导出符号时序任务约束。这补充了我们此前关于学习综合时序任务模型的工作：它在亚符号基础上整合符号与亚符号信息，同时仍保持我们此前方法的符号表达能力。我们将所提出的方法与此前的纯符号方法进行比较，结果表明我们能够复现甚至超越其性能。此外，我们还展示了亚符号时序任务约束如何同步原本为单手运动原语的动作，从而在人形机器人上实现双手行为。
英文摘要：
Learning task models of bimanual manipulation from human demonstration and their execution on a robot should take temporal constraints between actions into account. This includes constraints on (i) the symbolic level such as precedence relations or temporal overlap in the execution, and (ii) the subsymbolic level such as the duration of different actions, or their starting and end points in time. Such temporal constraints are crucial for temporal planning, reasoning, and the exact timing for the execution of bimanual actions on a bimanual robot. In our previous work, we addressed the learning of temporal task constraints on the symbolic level and demonstrated how a robot can leverage this knowledge to respond to failures during execution. In this work, we propose a novel model-driven approach for the combined learning of symbolic and subsymbolic temporal task constraints from multiple bimanual human demonstrations. Our main contributions are a subsymbolic foundation of a temporal task model that describes temporal nexuses of actions in the task based on distributions of temporal differences between semantic action keypoints, as well as a method based on fuzzy logic to derive symbolic temporal task constraints from this representation. This complements our previous work on learning comprehensive temporal task models by integrating symbolic and subsymbolic information based on a subsymbolic foundation, while still maintaining the symbolic expressiveness of our previous approach. We compare our proposed approach with our previous pure-symbolic approach and show that we can reproduce and even outperform it. Additionally, we show how the subsymbolic temporal task constraints can synchronize otherwise unimanual movement primitives for bimanual behavior on a humanoid robot.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
